Control Analysis with Modified LQR Method of Anti-Tank Missile with Vectorization of the Rocket Engine Thrust

This article approaches the issue of the optimal control of a hypothetical anti-tank guided missile (ATGM) with an innovative rocket engine thrust vectorization system. This is a highly non-linear dynamic system; therefore, the linearization of such a mathematical model requires numerous simplifications. For this reason, the application of a classic linear-quadratic regulator (LQR) for controlling such a flying object introduces significant errors, and such a model would diverge significantly from the actual object. This research paper proposes a modified linear-quadratic regulator, which analyzes state and control matrices in flight. The state matrix is replaced by a Jacobian determinant. The ATGM autopilot, through the LQR method, determines the signals that control the control surface deflection angles and the thrust vector via calculated Jacobians. This article supplements and develops the topics addressed in the authors’ previous work. Its added value includes the introduction of control in the flight direction channel and the decimation of the integration step, aimed at speeding up the computational processes of the second control loop, which is the LQR based on a linearized model.

Role of the Dorsal Posterior Parietal Cortex in the Accurate Perception of Object Magnitude in Peripheral Vision

Following superior parietal lobule and intraparietal sulcus (SPL-IPS) damage, optic ataxia patients underestimate the distance of objects in the ataxic visual field such that they produce hypometric pointing errors. The metrics of these pointing errors relative to visual target eccentricity fit the cortical magnification of central vision. The SPL-IPS would therefore implement an active “peripheral magnification” to match the real metrics of the environment for accurate action. We further hypothesized that this active compensation of the central magnification by the SPL-IPS contributes to actual object’ size perception in peripheral vision. Three optic ataxia patients and 10 age-matched controls were assessed in comparing the thickness of two rectangles flashed simultaneously, one in central and another in peripheral vision. The bilateral optic ataxia patient exhibited exaggerated underestimation bias and uncertainty compared to the control group in both visual fields. The two unilateral optic ataxia patients exhibited a pathological asymmetry between visual fields: size perception performance was affected in their contralesional peripheral visual field compared to their healthy side. These results demonstrate that the SPL-IPS contributes to accurate size perception in peripheral vision.

VR-Based Job Training System Using Tangible Interactions

Virtual training systems are in an increasing demand because of real-world training, which requires a high cost or accompanying risk, and can be conducted safely through virtual environments. For virtual training to be effective for users, it is important to provide realistic training situations; however, virtual reality (VR) content using VR controllers for experiential learning differ significantly from real content in terms of tangible interactions. In this paper, we propose a method for enhancing the presence and immersion during virtual training by applying various sensors to tangible virtual training as a way to track the movement of real tools used during training and virtualizing the entire body of the actual user for transfer to a virtual environment. The proposed training system connects virtual and real-world spaces through an actual object (e.g., an automobile) to provide the feeling of actual touch during virtual training. Furthermore, the system measures the posture of the tools (steam gun and mop) and the degree of touch and applies them during training (e.g., a steam car wash.) User-testing is conducted to validate the increase in the effectiveness of virtual job training.

Super-Resolved Recognition of License Plate Characters

Object detection and recognition are crucial in the field of computer vision and are an active area of research. However, in actual object recognition processes, recognition accuracy is often degraded due to resolution mismatches between training and test image data. To solve this problem, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique that improves object recognition accuracy. In detail, we collected a number of license plate training images through web-crawling and artificial data generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to image flips. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on representative test images and confirmed that the proposed super-resolution technique improves the accuracy of character recognition. For character recognition with the 4× magnification, the proposed method remarkably increased the mean average precision by 49.94% compared to the existing state-of-the-art method.

Rotating Object Detection in Remote-Sensing Environment

Deep learning models have become the mainstream algorithm for processing computer vision tasks. In object detection tasks, the detection box is usually set as a rectangular box aligned with the coordinate axis, so as to achieve the complete package of the object. However, when facing some objects with large aspect ratio and angle, the bounding box has to become large, which makes the bounding box contain a large amount of useless background information. In this study, a different approach is taken, using a method based on YOLOv5, the angle information dimension is increased in head part and angle regression added at the same time of the border regression, combining ciou and smoothl1 to calculate the bounding box loss, so that the resulting border box fits the actual object more closely. At the same time, the original dataset tags are also preprocessed to calculate the angle information of interest. The purpose of these improvements is to realize object detection with angles in remote-sensing images, especially for objects with large aspect ratios, such as ships, airplanes, and automobiles. Compared with the traditional object detection model based on deep learning, experimental results show that the proposed method has a unique effect in detecting rotating objects.

An Attempt to Model the Surface Pressures of Apples Using the Finite Element Method

Apples are the most popular fruits grown in Polish orchards. In order to obtain the best quality fruit, it is necessary to improve plantation maintenance, fruit harvesting, and processing. Given that many fruits are exposed to external factors, including forces that adversely affect their structure—causing them to crack, bruise, or crush—it is necessary to provide conditions that do not adversely affect their quality. Therefore, the aim of this article was to develop a simplified model of an apple that could be tested under different loads using the finite element method. The parameters of the model were selected to reflect the actual apple as accurately as possible. To assess the apples under impact load, as well as the construction of the FEM model, concrete and wooden substrates were used, where apples were dropped from height of 10 mm and 30 mm. Due to this research, an apple model was obtained that reflects the actual object very well (high R2 coefficient). In addition, the layering and distribution of surface pressures of the real and model objects from the distribution are presented. This shows that the constructed model corresponds to the behaviour of the biological material, subjected to loads in real conditions.

Improving the Robustness of Object Detection Through a Multi-Camera–Based Fusion Algorithm Using Fuzzy Logic

A single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic–based fusion. The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera’s image plane to another camera’s image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores (“high,” “ok,” and “low”) are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera–based fusion algorithm improved the overall robustness of the detection system.

THE APOLOGY OF THE WHOLE

At the level of the whole organism, an idea of the complexity of living things is formed as a combination of levels of organization (layers) of biological and virtual reality, which develops as a space for visualization (digitization) of living objects. New digital formats of living objects, coupled with the naturalistic ethics of obtaining them, create a trend towards a complete transition of biology to a quantitatively new level of obtaining biological information – information about the state of living biological objects. The development of digital biology contributes to an increasingly large-scale transition to the creation and analysis of virtual images of living biological objects, and at the same time "removes" the biologist from the actual object of research: a biologist can work with a virtual image and not destroy his research object. Digital naturalism appears, and, consequently, a digital "experiment" must also be expected, which will undoubtedly continue the eternal confrontation between naturalists and naturalists or vitalists with mechanists in the new techno environment.

A Novel Smith Predictive Linear Active Disturbance Rejection Control Strategy for the First-Order Time-Delay Inertial System

The control strategy research of the time-delay system is a focused issue in the control field. In order to furthermore improve the performance of the first-order time-delay inertial system, firstly, a new Smith predictor structure is proposed, which solves the constraint that the conventional Smith predictor needs to match the actual object model. Secondly, the performance and parameter function of the new Smith predictor are discussed in theory to provide the basis for parameter tuning. Finally, a new Smith predictor combined with linear active disturbance rejection control (LADRC) is proposed to solve the problem that the two input signals of the linear extended state observer (LESO) are not synchronized on the time scale, and the stability of the new Smith + LADRC time-delay control system is proved theoretically for known and unknown controlled complex objects. Simulation analysis is conducted to verify the robustness of the proposed strategy under the condition of the different parameters. The results indicate that the proposed strategy has better performance than the conventional method in response speed, overshoot, adjustment time, and stability.

DETEKSI JUMLAH KENDARAAN DENGAN ALGORITMA GAUSSIAN MIXTURE MODEL DI AREA JALAN RAYA

Road safety monitoring systems are developing at this time. The transportation sector is the object of research that continues to be developed and is always an interesting topic. Not only for security purposes and for statistical purposes for the road widening process that supports road user infrastructure, but the detection system is also useful for sales marketing statistics. In this research, propose a vehicle detection system that is useful for widening roads in a certain area or area so that it can reduce traffic congestion and accident rates. The proposed Gaussian Mixture Model method has several weaknesses, such as errors in background substitution with vehicles and failing to distribute the background with vehicle shadows. However, using morphological operations can overcome these problems. The results show a fairly good level of accuracy from the proposed method. It is only less effective when using video objects with poor lighting or at night because in the blob analysis process the detected vehicle objects do not match the actual object. But if the traffic flow is smooth and unidirectional, the proposed method is still acceptable.